IMPROVED DEVELOPMENT OF THE DOMAIN ONTOLOGY
FOR DIFFERENT USER PROFILES
Application Domain: Conformity Checking in Construction
Anastasiya Yurchyshyna
1
, Alain Zarli
CSTB, 290 Route des Lucioles, BP 209, 06904 Sophia Antipolis, France
Catherine Faron-Zucker, Nhan Le Thanh
1
I3S, UNSA-CNRS, 930 route des Colles, BP 145, 06903, Sophia Antipolis, France
Keywords: Development of the domain ontology, Conformity-checking modelling, User profiles, Semantic search,
Validation by usage.
Abstract: This paper presents a formal method for the development of the domain ontology for different user profiles
in the context of conformity checking in construction, which is developed to enrich our conformity-
checking model. We start by describing our research domain: the conformity-checking in construction. Then
we discuss some ontology-based approaches for formalising domain knowledge and particularly focus on
methods for the ontology development for different user profiles. In order to efficiently adapt our
conformity-checking ontology for different user profiles, we suggest a semantic approach for the improved
development of the domain ontology that takes into account the domain knowledge. This semantic approach
is based on three main ideas. First, we adapt the knowledge acquisition method developed for our
conformity-checking model. Second, we integrate a method of the context modelling of the domain
ontology applied by end users by integrating the results of the semantic search. Third, we develop an
approach for the adaptation of the domain ontology for different user profiles. Finally, we describe a web-
based prototype, the C3R (Conformity Checking in Construction: Reasoning) prototype that integrates our
semantic method of the improved development of the domain ontology for different user profiles.
1 INTRODUCTION
This paper presents a formal method for the
development of the domain ontology for different
user profiles. This work continues and extends our
research on the modelling of the conformity
checking process in construction, and particularly
focuses on the conceptual modelling of domain
knowledge and the usage-based validation by end
users.
The complexity of the conformity checking
problem can be explained by the following factors:
(i) the multidisciplinary of the components defining
the conformity checking (e.g. modeling of
construction regulations, reasoning on conformity),
(ii) the interdependence of various actors of the
construction domain; (iii) the large amount of the
non formalised expert knowledge guiding the
process, (iv) the great volumes of construction data
to be retrieved and maintained.
The central problem of the conformity checking
in construction is to automate the process of
checking whether a construction project (e.g. a
private house, a public building, a non-building
installation) is conform to a set of conformity
requirements described by regulation texts. The
semantic complexity of this problem requires an
expressive formalism for representing the
knowledge of the checking process. Recently,
multiple approaches for the development of
building-oriented ontologies have been developed:
e-COGNOS (El-Diraby et al. 2003), ifcOWL (Gehre
and Katranuschkov, 2007), buildingSMART (Bell
and Bjorkhaug, 2006). Despite the variety of these
approaches, these generic ontologies can be hardly
used for a specific aim of conformity-checking.
175
Yurchyshyna A., Zarli A., Faron-Zucker C. and Thanh N.
IMPROVED DEVELOPMENT OF THE DOMAIN ONTOLOGY FOR DIFFERENT USER PROFILES - Application Domain: Conformity Checking in Construction.
DOI: 10.5220/0001838901750183
In Proceedings of the Fifth International Conference on Web Information Systems and Technologies (WEBIST 2009), page
ISBN: 978-989-8111-81-4
Copyright
c
2009 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
To address these limitations, on our previous
research (Yurchyshyna et al., 2008a), we developed
a conformity-checking ontology, which integrates
not only building-related knowledge, but also the
knowledge on conformity regulation texts and the
expert knowledge on checking procedures.
Developed with the help of domain experts, mostly
from CSTB (Centre Scientifique et Technique du
Bâtiment), our conformity-checking ontology was a
key component of our conformity-checking model.
Complex and multidisciplinary, the construction
industry is a field of collaborative work and
communication of multiple actors, the so-called “key
players of the building-oriented market”, who form
the target audience for the development of the
construction sector and their needs define the
innovation process of the industry. The main key
players of the building-oriented market are as
follows:
architects generating data related to
different aspects of a building;
engineers responsible for generating data
related to a specified facility’s system of a
building;
contructors dealing with process-related
characteristics of a building (scheduling,
cost analysis, project management, etc.);
consumers of a building product;
building product manufacturers generating
supplementary data related to a building
product (e.g. physical and functional
characteristics, cost);
legal authorities formulating performance-
oriented rules of the development of a
building.
Obviously, different actors of the conformity
checking in construction have different needs and
understanding of the checking process. It also means
that they may interpret and use the knowledge from
the domain ontology in a different way (e.g. an
acoustic engineer may need a very detailed
“version” of the domain sub ontology concerning
acoustics, in contrast to a final user interested in
general non-detailed conformity recommendations).
The formalisation and the integration of the
domain tacit knowledge play an important role in the
checking modelling. In our previous work
(Yurchyshyna et al., 2008b) we describe our
approach for formalising expert knowledge, which is
also base on the conformity-checking ontology.
From the other hand, it is always interesting to
enrich the initial ontology by the knowledge that is
acquired from its usage. This research objective
motivated our research on the validation of the
conformity-checking ontology by integrating the
results of the semantic search.
The interdependence of the actors of the
application domain (i.e. conformity checking in
construction) is, however, an important factor to take
into account by the application of a generic domain
ontology. It this context, it is a real challenge to
enrich the approach for the ontology development by
the knowledge of its usage by different user groups
and to refine the ontology for different user profiles.
This research objective motivates our work on the
development of our semantic method of the
improved development of the domain ontology for
different user profiles, the DOUP method.
The paper is organized as follows. In next
section, we discuss some ontology-based approaches
for formalising domain knowledge and particularly
focus on methods for the ontology development for
different user profiles. Section 3 represents our
semantic approach for the improved development of
the domain ontology for different user profiles (the
DOUP-method) and details its three levels. In
section 4, we describe the C3R prototype aiming to
illustrate the feasibility of our approach. Finally, we
describe the ongoing works and the perspectives of
our research.
2 TOWARDS THE
DEVELOPMENT OF THE
DOMAIN ONTOLOGY FOR
DIFFERENT USER PROFILES
Our research on the development of the domain
ontology for different user profiles is based on three
scientific axes. First, we study the main problems of
the development of a domain ontology, which is
characterised by a large amount of the tacit
knowledge. Second, we focus on the factors defining
the different usage of the same generic domain
ontology by different user groups. Third, we study
the methods of the ontological modelling oriented
different user profiles.
As a general rule, the development of an
industry-oriented domain ontology is characterised
by the following factors.
First, a large amount of the knowledge to be
formalised is tacit (Polanyi, 1983). For example, the
conformity-checking in construction is characterised
by: (i) the “know-what” knowledge of the
construction industry commonly known by
architects; and (ii) the “know-how” knowledge of
the checking process shared by conformity experts.
WEBIST 2009 - 5th International Conference on Web Information Systems and Technologies
176
It is thus indispensable to explicit and interpret such
tacit domain knowledge formalising it.
Second, the development of the domain ontology
is driven by its (future) application. For that reason,
the development of a domain ontology is often a part
of some more global research task (e.g. conformity-
checking modelling, semantic search, etc.).
Moreover, the formalisms used for development
should be seamlessly interconnected with the
formalisms of these research tasks and should be
based on interoperable standards. For example, the
development of the buildingSMART ontology (Bell
and Bjorkhaug, 2006) is coordinated with the
elaboration of the SMARTcodes™, the code
provisions for code compliance checking
(Smartcodes, 2008), as well as regulation-centric
knowledge representation formalisms define
conceptual architecture of the conformance
assistance framework (Kerrigan and Law, 2005).
Third, before formal representation, the domain
knowledge should be first interpreted by domain
experts. Even if the domain experts are the
professionals of the domain, it is obvious that such
interpretation remains rather subjective and/or
partial. For this reason, it is important to validate the
acquired domain ontology by usage.
Fourth, from the different point of view, the
expert interpretation sometimes differs from the
understanding of end users, who may find the
knowledge “not adequate” and “difficult to use”, but
fail to express the exact meaning of the concepts
used (e.g. a user may find it difficult to distinguish
between “main door” and “entrance door”, but does
not use these two concepts in the same way).
Fifth; the domain ontology is often defined in a
specified context (Hernandez et al, 2007), which
should be then validated by usage.
The second axe of our analysis is devoted to the
practical usage of the generic domain ontology by
different user groups. Such usage may cause
problems for the following reasons: (i) the
interpretation of the domain knowledge by end users
may be different from the interpretation of domain
experts; (ii) different groups of end users may have
different scope of interest (e.g. an architect and a
legal authority need different level of detailing the
conformity-related construction ontology); (iii) a
large amount of knowledge remains tacit. In other
words, it may be difficult for end users to define
how they really need to use this knowledge (e.g. in
the case of checking the conformity of a public
building, a user may interest only in checking its
accessibility, but not the acoustic requirements,
which are the part of the global conformity-
checking).
The problem of the development of a domain
ontology for different user profiles represents our
third research axe. A general approach for
personalising the user's environment and integrating
the user profiles into the development of information
services is discussed in (Sutterer et al, 2008). The
main methods for the automatic creating and
application of user profiles are discussed in (Gauch
et al, 2007). These methods allow integrating search
results tailored to individual users to more complex
systems and thus to personalise the application od
such systems. In (Sieg et al, 2007), the authors
propose a general approach for representing the user
context by assigning interest scores to existing
concepts in a domain ontology.
We focus on these three research axes aiming the
development of the domain ontology for different
user profiles to define our semantic approach for the
improved development of the domain ontology for
conformity-checking in construction, which allows
the personalisation of the domain knowledge for
different user profiles.
3 SEMANTIC APPROACH FOR
THE IMPROVED
DEVELOPMENT OF THE
DOMAIN ONTOLOGY
Our semantic approach for the improved
development of the domain ontology for different
user profiles (the DOUP-method) has three levels:
1. Our knowledge representation and
acquisition method (the KRA-method)
developed for our conformity-checking
model.
2. Our method of context modelling of the
ontology by integrating the results of
semantic search (the CMV-method).
3. Our approach for the adaptation of the
domain ontology for different user profiles
(the ECMV-method).
3.1 Knowledge Representation and
Acquisition Method
We adopt the ontological approach and the semantic
web technologies (Berners-Lee, 2001) to develop the
knowledge representation and acquisition method
(the KRA-method, cf. Figure 1) that allows us to
represent complex and multidisciplinary knowledge
characterising the conformity-checking process in
construction. In this section, we briefly describe the
IMPROVED DEVELOPMENT OF THE DOMAIN ONTOLOGY FOR DIFFERENT USER PROFILES - Application
Domain: Conformity Checking in Construction
177
main ideas of our knowledge representation and
acquisition method. A more detailed explanation and
corresponding examples could be found in
(Yurchyshyna et al, 2008a).
Figure 1: Knowledge representation and acquisition
method.
The first phase of our method aims to acquire the
formal representations of conformity requirements
expressed by technical construction norms. We have
developed a base of accessibility queries by
extracting them from the CD REEF, the electronic
encyclopaedia of construction texts and regulations,
edited by CSTB, and by formalising them as
SPARQL queries in collaboration with construction
experts from the CSTB.
The second phase aims at the semi-automatic
development of an ontology oriented conformity
checking on the basis of the concepts from the
acquired SPARQL queries. These concepts are
organized as hierarchies and described in the OWL-
Lite language. The acquired ontology is then
enriched by non-IFC concepts from formalized
conformity queries. The intervention of domain
experts is required in this case to define new non-
IFC concepts in terms of the checking ontology (e.g.
GroundFloor class is defined by a resource of type
IfcBuildingStorey situated on the level of entering
into a building).
The third phase is dedicated to the acquisition of
a construction project representation oriented
conformity checking. This representation is based on
its initial ifcXML representation and is guided by
the acquired conformity-checking ontology. We
develop an XSLT stylesheet that filters this ifcXML
to extract the data relative to the conformity
checking ontology and organizes them as RDF
triples. The acquired RDF is then enriched with non-
IFC concepts extracted from conformity queries
(e.g. a project representation is enriched by
GroundFloor concept calculated on the basis of its
initial IFC-based data (e.g. IfcDoor, IfcStair, etc.)
The acquired queries, however, contain only
conformity constraints, but have no supplementary
information, guiding the checking process: e.g. the
scheduling of queries. The forth phase of our method
aims thus at the development of semantic annotation
of conformity queries. We propose a special RDF
annotation of a query, developed according to its
tag-based context: possible values for certain tags
are concepts/properties of the conformity-checking
ontology.
To do it, we combine two main methods of
document annotation: annotation by content of the
document and annotation by its external sources
(Mokhtari and Dieng-Kuntz, 2008). First, we
annotate a query by its content. To do this, we define
a set of key concepts of this query, which describe
what is really checked by this conformity
requirement. In other words, we define keyConcept
tag in the RDF annotation of a query, which value is
a list of concepts from the conformity-checking
ontology extracted from the SPARQL representation
of this query. We remark also that there is a
semantic correspondence between different types of
knowledge used for query annotation. For example,
a conformity query defining the physical dimensions
of a door is annotated by a Door concept from our
conformity-checking ontology.
Second, we annotate a conformity query
according to external sources. Such annotation
allows representing different types of knowledge.
First, they are characteristics of the regulation text
from which the query was extracted: (i) thematic
(e.g. accessibility); (ii) regulation type (e.g.
circular); (iii) complex title composed of the title,
publication date, references, etc.; (iv) level of
application (e.g. national), (v) destination of a
building (e.g. private house). Second, they are
characteristics of extraction process: (i) article, (ii)
paragraph from which a query was extracted, (iii)
current number (e.g. 3 query of 1 paragraph of Door
article). Third, it is formalised expert knowledge:
tacit « common knowledge » on the process of
conformity-checking that is commonly applied by
domain experts: (i) knowledge on domain and sub
domain of the application of a query (e.g. Stairs); (ii)
knowledge on checking practice (e.g. if a room is
adapted, it is always accessible). Fourth, it is the
application context of a query. This group specifies
the aspects of query application for certain use cases.
For example, the requirements on the maximal
height of stairs handrail vary from 96 cm (for adults)
to 76 cm (for kids). In this case, it is important to
know the destination of a building (e.g. school).
Characteristics and possible values of the first
two groups are automatically extracted from the CD
WEBIST 2009 - 5th International Conference on Web Information Systems and Technologies
178
REEF. The knowledge described by the last two
groups is defined partially and/or has to be explicitly
formalised by domain experts.
Figure 2: Conformity-checking model.
The knowledge acquired by the KRA-method is
then used in our conformity-checking model that is
based on the analysis of matchings between the
representations of a construction project and of
conformity queries (Yurchyshyna et al, 2008a, cf.
Figure 2).
3.2 Context Modelling of the Domain
Ontology by Integrating the Results
of Semantic Search
According to the KRA-method, the conformity-
checking ontology is developed with the help of
domain experts and does not depend on the
conformity-checking process. All concepts and
relations of the ontology are defined and validated
by domain experts before the checking process and
can not be changed in the process of checking.
Domain experts also formulate rules of definition of
new concepts, context rules and, in general, they
validate the whole knowledge base of the
conformity-checking process.
In some cases, such definitions can be partial or
inadequate, and it does not represent the real usage-
driven conformity-related knowledge of the
checking process: even the definition of domain
experts is not sufficient to represent the whole
complexity of the checking knowledge. It is thus
important to propose an approach of the acquisition
of another type of the checking knowledge: the
knowledge on the checking practices, which turns
out explicit thanks to a large number of checking
operations by different end non-expert users.
To do this, we developed an approach of the
context-based modelling of the domain ontology,
which is validated by usage, the CMV-method
(Yurchyshyna et al, 2008c). In other words, we
proposed an approach for the evaluation of the
semantic proximity of different concepts/relations of
the conformity-checking ontology, according to the
interpretation of end users.
The CVM-method is based on the semantic
annotations of conformity queries. It aims to analyse
the simultaneous choice of the queries, which are
annotated by the same key concepts, and thus to
define the semantic similarity between these
concepts.
In the KRA-method, the semantic annotations of
conformity queries are developed according to the
tag-based context: possible values for certain tags of
semantic annotations of queries are
concepts/properties of the conformity-checking
ontology.
The following example illustrates an annotation
of a query by the door concept of the conformity-
checking ontology.
<rdf:RDF xmlns:ontoCC=”domain.owl#”
<Annotation rdf:ID=””> ...
<domaineApplication>
<ontoCC:Door/>
</domaineApplication> ...
</Annotation>
</rdf:RDF>
Our work on modelling the domain ontology for
conformity checking in construction is conducted
under the Semantic Web vision, which guarantees
more advances capabilities for processing the
knowledge. In particular, we also interest at more
advanced search, the so called semantic search that
gives better results in comparison to traditional
search mechanisms.
The semantic annotation of conformity queries
allows us to propose a user more detailed selection
of queries to be checked. For example, for a user
interested in checking the conformity of a door, we
can propose the semantic search that will give a
semantically richer result: it will interrogate the
domain ontology to define not only the queries
annotated by Door, but also all the corresponding
ones (its subclasses Entrance, EntranceDoor,
FrontDoor, AccessibleEntrance). It also means that
a user can obtain a semantically consistent answer
about the content of the conformity query before
executing it – only by its RDF annotation – and thus
to identify what he really wishes to check.
Technically, such semantic search is based on the
execution of the following SPARQL query against a
base of RDF semantic annotations.
IMPROVED DEVELOPMENT OF THE DOMAIN ONTOLOGY FOR DIFFERENT USER PROFILES - Application
Domain: Conformity Checking in Construction
179
PREFIX a:<annotations.owl#>
PREFIX ontoCC: <domain.owl#>
SELECT ?s ?nQuery ?appValue ?cCl
WHERE { ?s direct::rdfs:subClassOf ?cCl
FILTER(?s ^ontoCC:)
?nQuery a:domaineApplication ?appValue
?appValue rdf:type ?cCl
FILTER (?cCl ~ 'door') }
In our example, the search of “door” expression
will give the list of queries, which application
domain contains “door” and classifies them
according to the conformity-checking ontology. In
comparison to the traditional search (that results
with the only answer “door”), the semantic search
will detail the application domain of found queries
and classifies them into subclasses: (i) “door”; (ii)
“entrance door”, “front door”, “entrance”; (iii)
“accessible entrance” (cf. Figure 3).
Figure 3: Door and its subclasses.
The advantage of such semantic search is that it
is defined according to the general domain
knowledge of the construction industry, formalised
in the conformity-checking ontology, which is
independent of an end user, but helps him to detail
the search of corresponding conformity requirements
and thus to refine the algorithms of their application
during this process.
Another advantage of our approach for the
semantic search of conformity queries is that the
results of the semantic search followed by the user
selection of a query can be then used to validate the
initial domain ontology.
To illustrate these ideas by an example, let us
take three subclasses of a Door class: FrontDoor,
Entrance and EntranceDoor, which are defined
equivalent in the conformity-checking ontology.
They are also used as key concepts to annotate
conformity queries (e.g. these three concepts
annotate the query “an entrance door of any
building should be accessible to disabled persons”).
According to our model, for the checking of the
conformity of an entrance door of a building, a
construction project should be checked to the queries
annotated by all these three concepts. A full list of
these queries will be thus proposed to an end user.
In some cases, this list turns out redundant when
an end user has no interest in some specific queries
(e.g. the one concerning the luminosity of an
entrance door of a school). It is, therefore, important
to evaluate the cohesion between the queries chosen
and rejected by an end user and the corresponding
key concepts annotating these queries. For example,
we can notice that queries annotated by Entrance
and EntranceDoor are chosen more frequently than
the ones annotated by Door. Intuitively, Entrance
and EntranceDoor are semantically closer than
Entrance and Door (cf. Figure 4).
Figure 4: Semantic distances between subclasses of Door.
To propose a formal definition of the validation
of the conformity-checking ontology by usage, we
first define our approach on the evaluation of the
concepts of the conformity-checking ontology. It is
based on three main criteria (Karoui et al, 2007)
adapted for the conformity-checking problematic: (i)
credibility degree: we suppose that all concepts and
properties of the conformity-checking ontology are
defined by construction experts, their definitions are
pertinent and correct with the credibility degree
equal to 1; (ii) cohesion degree: we suppose that our
conformity-checking ontology is homogeneous:
there are subclasses of a class which are declared
equivalent by domain experts (e.g. door, entrance,
entranceDoor); (iii) eligibility degree: concepts and
relations are defined by experts and added to the
conformity-checking ontology, if they are necessary
for the formalization of conformity queries.
Our approach of the context-based validation of
the conformity-checking ontology by usage is
developed according to the same criteria, in order to
keep the semantic consistency of the conformity-
checking ontology: (i) credibility degree: no
concepts or relations can be defined by end non-
expert users; (ii) cohesion degree: the distance
between the equivalent concepts is then recalculated
according to the frequency of their simultaneous
choice by end non-expert users (e.g. Entrance and
EntranceDoor are chosen more often); (iii)
eligibility degree: if some classes of semantically
close concepts are defined, it can be interesting to
identify the concept characterising the whole class,
e.g. EntranceDoor for the class containing Entrance,
AccessibleEntrance, FrontDoor, etc. By identifying
the representative concept of the class, we can refine
WEBIST 2009 - 5th International Conference on Web Information Systems and Technologies
180
the semantic annotation of the corresponding queries
(for example, annotating them only by this concept)
and, consequently, the algorithms of expert
reasoning (for example, we do not need to schedule
queries which are annotated by the concepts of the
same class).
To model the semantic distances in the
conformity-checking ontology, we base on the
calculating of the semantic similarity in content-
based retrieval systems (El Sayed et al. 2007) and by
adapting the approach of the “intelligent evaluation”
(Karoui et al, 2007) of ontological concepts.
Currently, we work on the detailed development of
the conceptual approach for the evaluation of the
concepts of the conformity-checking ontology.
3.3 Adaptation of the Domain Ontology
for Different User Profiles
Our method for the development and usage-based
validation of the domain ontology remains still
generic and not adapted to the variety of different
actors of the construction domain. For this reason, it
is a real challenge to propose an approach for
adapting it for different user profiles: e.g. architect,
electric engineer, legal authority, etc.
In order to adapt the acquired domain ontology
for different user profiles, we propose to enrich our
CMV-method by personalising it for different user
profiles: the ECMV-method.
Our ECMV-method contains two main steps.
First, we identify the groups of users and the
corresponding user profiles. For each user profile,
we create a copy of the initial generic domain
ontology: e.g. the conformity-checking ontology for
(i) architects; (ii) electric engineers; (iii) conformity-
checking experts; and (iv) end non-professional
users.
Figure 5: Semantic distances between subclasses of Door
for different user profiles.
Second, we define the scope of interest for each
user profile. To do this, we apply the CMV-method
for each group of users and modify their copy of the
domain ontology according to its usage by the
corresponding end users. As result, we generalise or
detail the domain ontology according to the scope of
interest of user profiles. For example, it is important
only for an architect to distinguish between different
types of entrances (cf. Figure 5).
It is important to underline that the ECMV-
method guarantees the coherence and semantic
consistency between the generic domain ontology its
facets developed for different user profiles. This
coherence is based on the following aspects: (i)
credibility degree: the credibility of the facets of the
initial domain ontology is 0; the users can only
refine the distances between the concepts of the
initial domain ontology, but they can not create new
concepts; (ii) cohesion degree: the distance between
the synonym concepts is recalculated according to
the frequency of their simultaneous choice by users
of the same user profile (we do not aim at
establishing correspondences between different user
profiles); (iii) eligibility degree: if users of the same
user profile define semantically close concepts, these
concepts are grouped by a representative concept
which is the closest super class of these semantically
close concepts (e.g. Entrance, EntranceDoor,
FrontDoor classes of the initial domain ontology are
grouped by Entrance class of the ontology facet for
electric engineers).
4 IMPLEMENTATION:
ENRICHMENT OF THE C3R
PROTOTYPE FOR DIFFERENT
USER PROFILES
In our previous work on the development of the
conformity-checking model (Yurchyshyna et al.,
2008a), we have developed the C3R (Conformity
Checking in Construction with the help of
Reasoning) system (cf. Figure 6) that implements
the algorithms of reasoning by expert rules
according to organised conformity queries. For the
checking operation, C3R relies on the semantic
search engine CORESE (Conceptual Resource
Search Engine) (Corby, Faron-Zucker, 2007), which
implements RDF, RDFS and SPARQL languages
and answers SPARQL queries asked against an
RDF/OWL Lite knowledge base (Sowa, 1984); and
SEWESE (Sewese, 2008), the JSP/Servlet/Corese
environment for building Semantic Web
applications.
The main components of the C3R prototypes are:
(i) the knowledge acquisition module (query
IMPROVED DEVELOPMENT OF THE DOMAIN ONTOLOGY FOR DIFFERENT USER PROFILES - Application
Domain: Conformity Checking in Construction
181
formaliser, ontology editor; construction project
extractor); (ii) the reasoning module (checking
reasoner enabled by the CORESE engine; query
scheduler, conformity report generator); (iii) the
module on capitalisation of context knowledge
(query base generator; annotation editor; expert
reasoning explorer; formaliser of usage-based
knowledge).
Figure 6: C3R infrastructure: general view.
For the C3R prototype, we have defined a
conformity-checking ontology that currently
comprises around 2200 concepts and 1600
properties. The conformity-checking ontology is
written in the OWL-Lite language, which is rather
expressible and, at the same time, decidable. We
also define about 50 definition rules describing new
concepts and properties with the help of the ones
from the conformity-checking ontology.
To develop a base of conformity queries for the
validation of our approach, we chose 9 regulation
texts on the accessibility of public buildings (French
regulation base). These regulation documents
represent different classes of regulation texts (e.g.
norm, circular) and describe the accessibility
constraints of different entities: doors, routes,
signalisation, etc. With the help of CSTB experts,
we have identified about 350 simple text conformity
queries that resume these 9 regulation texts, which
are partially interpreted (around 65%). Other 35% of
identified queries are classified as non interpretable
and are not formalised. For practical validation of
our approach, we currently formalised and tested
about 100 conformity queries as SPARQL queries.
To adapt the C3R prototype for different user
profiles, we define 3 user profiles: architects,
engineers, and owners/non-professional end users.
For each user profile, we create a facet of the
conformity-checking ontology and define. The
calculation of the semantic similarity between the
concepts of the conformity-checking ontology
according to the DOUP-method is not implemented
yet. It is an objective of our future work on the
incremental implementation of the C3R prototype.
5 CONCLUSIONS
We presented a formal method for the development
of the domain ontology for different user profiles in
the context of conformity checking in construction.
Our semantic approach for the improved
development of the domain ontology for different
user profiles (the DOUP-method) comprises three
components: (i) the KRA-method aiming to
represent the knowledge for the conformity-
checking modelling; (ii) the CMV-method aiming
context modelling of the ontology by integrating the
results of semantic search of a query to check; and
(iii) the ECMV-method, which adapts the CMV-
method of the development of a domain ontology for
different user profiles. We also described the
conceptual architecture of the C3R prototype and
presented the current work on its implementation, to
illustrate feasibility of our approach.
One possible limitation of our work is that we do
not establish the semantic correspondences between
different facets of the domain ontology. This very
interesting research problem is not taken into
account by our semantic approach for the improved
development of the domain ontology for different
user profiles, and can be seen as a probable axe for
future research.
Our future works focus on the further
incremental development of the conformity-
checking ontology and the C3R prototype, as well as
their evaluation by domain experts and end users. In
particular, we will detail the DOUP-method to adapt
the C3R prototype for different user profiles, as well
as to create different facets of the conformity
checking ontology.
REFERENCES
Bell, H., Bjorkhaug, L., 2006. A buildingSMART
Ontology. In ECPPM-2006, European Conference on
Product and Process Modelling, Valencia, Spain, 185-
190
Brézillon, P., 2007. Context Modeling: Task Model and
Model of Practices. In CONTEXT 07, 6th
International and Interdisciplinary Conference on
Modeling and Using Context.
Berners-Lee, T., 2001. Reflections on Web Architecture.
Conceptual Graphs and the Semantic Web, 2001,
available at http://www.w3.org/DesignIssues/CG.html
Corby, O., Faron-Zucker, C., Implementation of SPARQL
Query Language based on Graph Homomorphism. In
15th International Conference on Conceptual
Structures (ICCS’2007).
WEBIST 2009 - 5th International Conference on Web Information Systems and Technologies
182
El-Diraby, T., Fiès, B., Lima, C. 2000. D3.6: The e-
COGNOS Ontology V1.1.0 - WP3. E-Cognos project
IST-2000-28671.
El Sayed A., Hacid H., Zighed A., 2007. A Context-
Dependent Semantic Distance Measure, In 19th
International Conference on Software Engineering
and Knowledge Engineering (SEKE), Boston, USA
Gauch, S., Speretta, M., Pretschner, A., 2007. Ontology-
Based User Profiles for Personalized Search, in A
Handbook of Principles, Concepts and Applications in
Information Systems, Integrated Series in Information
Systems (Ed: Sharman, R., Kishore, R., Ramesh, R.),
Volume 14, pp. 665-694
Gehre, A., Katranuschkov, P., 2007. InteliGrid
Deliverable D32.2 – Ontology Services,
www.inteliGrid.com
Hernandez, N., Mothe, J., Chrisment, C., Egret, D., 2007.
Modeling Context through Domain Ontologies. In
Information Retrieval 10 (2).
Karoui L., Aufaure M.-A., Bennacer N., 2007. Contextual
Concept Discovery Algorithm. In FLAIRS-20 the 20th
International FLAIRS Conference, AAAI Press.
Kerrigan, S. L., Law, K. H., 2005. Regulation-Centric,
Logic-Based Conformance Assistance Framework,
Journal of computing in civil engineering, ASCE,
19(1): 1-15, 2005.
Mokhtari, N., Dieng-Kuntz, R., 2008. Extraction et
exploitation des annotations contextuelles. In
EGC'2008, Volumes I, Sophia Antipolis, France, 7-18
Polanyi, M., 1983, The Tacit Dimension. Doubleday &
Co, 1966. Reprinted Peter Smith, Gloucester, Mass.
Sewese, 2008, available at http://www-
sop.inria.fr/teams/edelweiss/wiki/wakka.php?wiki=Se
wese
Sieg, A., Mobasher, B., Burke R., 2007. Ontological User
Profiles as the Context Model in Web Search, In
IEEE/WIC/ACM Intl Conference on Web Intelligence
and Intelligent Agent Technology, pp. 91 – 94
Smartcodes, 2008, available at
http://www.iccsafe.org/SMARTcodes/
Smart Market report, McGraw
Hill Construction, 2007, available at
http://www.iccsafe.org/SMARTcodes/SMRI_Final.pdf
Sutterer, M.; Droegehorn, O.; David, K, 2008. UPOS:
User Profile Ontology with Situation-Dependent
Preferences Support, In 1st Intl Conference on
Advances in Computer-Human Interaction, 230 – 235.
Yurchyshyna A., Faron-Zucker C., Le Thanh N., Zarli A.,
2008a. Ontological Approach for the Conformity-
Checking Modelling in Construction, In 10th
International Conference on Enterprise Information
System, ICEIS2008, Barcelone, Spain, p.139
Yurchyshyna, A., Faron-Zucker, C., Le Thanh N., Zarli,A.
2008b. Towards the Knowledge Capitalisation and
Organisation in the Model of Conformity-Checking
Process in Construction, Lecture Notes in Computer
Science; Springer Berlin / Heidelberg, Vol. 5177/2008
Yurchyshyna A., C. Faron-Zucker, N. Le Thanh, A. Zarli.
2008c. Towards an ontology-based approach for
formalising expert knowledge in the conformity-
checking model in construction, In ECPPM2008,
Sophia Antipolis, France
IMPROVED DEVELOPMENT OF THE DOMAIN ONTOLOGY FOR DIFFERENT USER PROFILES - Application
Domain: Conformity Checking in Construction
183